CVpods简介

cvpods, a versatile and efficient codebase for many computer vision tasks: classification, segmentation, detection, self-supervised learning, keypoints and 3D(classification / segmentation / detection / representation learing), etc. The aim of cvpods is to achieve efficient experiments management and smooth tasks-switching.

 

Build cvpods from source

Make sure GPU is available on your local machine.

# Install cvpods with GPU directly 
pip install 'git+https://github.com/Megvii-BaseDetection/cvpods.git' --user

# Or, to install it with GPU from a local clone:
git clone https://github.com/Megvii-BaseDetection/cvpods.git
pip install -e cvpods --user 

# Or, to build it without GPU from a local clone:
FORCE_CUDA=1 pip install -e cvpods --user

 

Get Start

Here we use coco object detection task as an example.

# Preprare data path
ln -s /path/to/your/coco/dataset datasets/coco

# Enter a specific experiment dir 
cd playground/retinanet/retinanet.res50.fpn.coco.multiscale.1x

# Train
pods_train --num-gpus 8
# Test
pods_test --num-gpus 8 \
    MODEL.WEIGHTS /path/to/your/save_dir/ckpt.pth # optional
    OUTPUT_DIR /path/to/your/save_dir # optional

# Multi node training
## sudo apt install net-tools ifconfig
pods_train --num-gpus 8 --num-machines N --machine-rank 0/1/.../N-1 --dist-url "tcp://MASTER_IP:port"

 

教程和API链接：

We provide a detailed tutorial, which covers introduction, usage, and extend guides in cvpods_tutorials. For all API usages, please refer to our documentation.